Language-Aware Distillation for Multilingual Instruction-Following Speech LLMs with ASR-Only Supervision

Abstract:Speech Large Language Models (LLMs) that understand and follow instructions in many languages are useful for real-world interaction, but are difficult to train with supervised fine-tuning, requiring large, task-specific speech corpora. While recent distillation-based approaches train performant English-only Speech LLMs using only annotated ASR data by aligning text and speech using only a lightweight projector, these models under-perform when scaled to multilingual settings due to language interference in the shared projector. We address this by introducing language-aware distillation using a query bank and a gating network that selects or mixes query tokens using a Q-Former projector. Our approach shows gains of 14% over matched multilingual distillation baselines on instruction following. We further synthesize Audio-MLQA, a multilingual spoken QA benchmark built on MLQA with high-quality TTS questions. Our best model improves over existing Speech LLM baselines by 32% on Audio-MLQA.

Computation and Language (cs.CL)

arXiv:2603.07025 [cs.CL]

(or arXiv:2603.07025v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.07025 Focus to learn more arXiv-issued DOI via DataCite

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